Paper ID: 2401.03742

Flowmind2Digital: The First Comprehensive Flowmind Recognition and Conversion Approach

Huanyu Liu, Jianfeng Cai, Tingjia Zhang, Hongsheng Li, Siyuan Wang, Guangming Zhu, Syed Afaq Ali Shah, Mohammed Bennamoun, Liang Zhang

Flowcharts and mind maps, collectively known as flowmind, are vital in daily activities, with hand-drawn versions facilitating real-time collaboration. However, there's a growing need to digitize them for efficient processing. Automated conversion methods are essential to overcome manual conversion challenges. Existing sketch recognition methods face limitations in practical situations, being field-specific and lacking digital conversion steps. Our paper introduces the Flowmind2digital method and hdFlowmind dataset to address these challenges. Flowmind2digital, utilizing neural networks and keypoint detection, achieves a record 87.3% accuracy on our dataset, surpassing previous methods by 11.9%. The hdFlowmind dataset, comprising 1,776 annotated flowminds across 22 scenarios, outperforms existing datasets. Additionally, our experiments emphasize the importance of simple graphics, enhancing accuracy by 9.3%.

Submitted: Jan 8, 2024